打破3D数据集瓶颈：从零开始快速可扩展生成对齐3D资产用于类别级6D位姿估计与机器人抓取
Breaking the 3D Dataset Bottleneck: Fast Scalable Generation of Aligned 3D Assets from Scratch for Category 6D Pose Estimation and Robotic Grasping
提供了一种从文本提示自动生成大规模规范对齐3D网格和6D位姿标注的流水线，可直接用于训练类别级6D位姿估计和机器人抓取模型，解决3D数据稀缺问题。
原文依据 We introduce the first scalable, automated framework that generates complete category-level 6D pose datasets directly from text prompts
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论尽管2D视觉已被ImageNet等大规模数据集彻底变革，3D视觉仍受限于高质量、规范对齐数据的稀缺。我们提出了首个可扩展的自动化框架，能够直接从文本提示生成完整的类别级6D位姿数据集，无需依赖现有3D资产。该方法克服了关键挑战：(1) 通过受控的文本到图像到3D流水线确保可靠、可扩展的资产生成；(2) 通过深度条件生成强制内置规范对齐，达到96%的位姿一致性率；(3) 通过混合现实渲染实现大规模6D标注。该流水线每个对象在3分钟内生成高质量对齐3D网格——相比传统扫描提速5-20倍。我们为Omni6Dpose基准中的153个类别各生成超过1,000个实例，最终得到153,000个对齐网格——相比之前对齐真实世界数据集，每个类别的实例数增加超过40倍。大量评估表明，在NOCS 6D位姿基准上具有竞争力的零样本仿真到现实迁移能力，并且在仿真和真实世界零样本迁移中均展现出优越的机器人抓取性能，其中对齐网格被证明是成功的关键。我们发布了最大的公开可用对齐3D网格数据集、最大的类别级6D位姿数据集、抓取仿真环境以及开源流水线，为3D理解的基础模型迈出了关键一步，并实现了从零开始高效、无限地生成任务特定3D数据。代码和数据集见 https://genomni3d.github.io/
英文原文摘要 1660 字符
While 2D vision has been revolutionized by large-scale datasets like ImageNet, 3D vision remains constrained by the scarcity of high-quality, canonically aligned data. We introduce the first scalable, automated framework that generates complete category-level 6D pose datasets directly from text prompts, bypassing the need for existing 3D assets. Our method overcomes key challenges by: (1) ensuring reliable, scalable asset generation via a controlled text-to-image-to-3D pipeline; (2) enforcing built-in canonical alignment through depth-conditioned generation, achieving a 96% pose consistency rate; and (3) enabling large-scale 6D annotation via mixed reality rendering. The pipeline produces high-quality, aligned 3D meshes in under 3 minutes per object--a 5-20x speedup over traditional scanning. We generate over 1,000 instances for each of the 153 categories in the Omni6Dpose benchmark, culminating in 153,000 aligned meshes--a >40x increase in instances per category over previous aligned real-world datasets. Extensive evaluation demonstrates competitive zero-shot sim2real transfer on the NOCS 6D pose benchmark and superior robotic grasping performance in both simulation and real-world zero-shot transfer, where aligned meshes prove essential for success. We release the largest publicly available aligned 3D mesh dataset, largest category-level 6D pose dataset, grasping simulation environments, and open-source pipeline, providing a critical step toward foundation models for 3D understanding and enabling efficient, unlimited generation of task-specific 3D data from scratch. The code and datasets can be found at https://genomni3d.github.io/